RL has recently gained significant attention in vehicular networks, with applications ranging from storage of data [1]} to controlling of information flow in sensing networks [2]}, thanks to its ability to handle tasks in dynamic, time-varying environments. The resource allocation task challenges the mainstream approach of formulating an optimization problem and solving it (sub-)optimally, because of the highly dynamic nature of vehicular networks [3]}. RL offers an alternative solution, which interacts with, and adapts its actions to the unknown environment. Further, sequential decision-making encountered in resource allocation tasks is a native functionality of RL[4]}.
